Optimal operational control for complex industrial processes
Tài liệu tham khảo
Adetola, 2010, Integration of real-time optimization and model predictive control, Journal of Process Control, 20, 125, 10.1016/j.jprocont.2009.09.001
Alvarez, 2010, Robust integration of real time optimization with linear model predictive control, Computers and Chemical Engineering, 34, 1937, 10.1016/j.compchemeng.2010.06.017
Bartusiak, R. D. (2005). NMPC: A platform for optimal control of feed- or product-flexible manufacturing. In Preprints international workshop on assessment and future directions of NMPC (pp. 3–14). Freudenstadt.
Bischoff, 2001, Vol. 26
Chai, 2011, Hybrid intelligent control for optimal operation of shaft furnace roasting process, Control Engineering Practice, 19, 264, 10.1016/j.conengprac.2010.05.002
Chai, 2008, Hybrid intelligent optimal control method for operation of complex industrial processes, Acta Automatica Sinica, 34, 505, 10.3724/SP.J.1004.2008.00505
Chai, 2007, Hybrid intelligent control for hematite high intensity magnetic separating process, Measurement and Control, 40, 171, 10.1177/002029400704000601
Chai, 2007, Intelligent work-situation fault diagnosis and fault-tolerant system for roasting process of shaft furnace, Proceedings of the ImechE, Part I, Journal of Systems and Control Engineering, 221, 843
Chai, 2011, Data-based virtual unmodelled dynamics driven multivariable nonlinear adaptive switching control, IEEE Transactions on Neural Networks, 22, 2154, 10.1109/TNN.2011.2167685
Chai, 2013, Integrated network based model predictive control for set point compensation in industrial processes, IEEE Transactions on Industry Informatics, 9, 417, 10.1109/TII.2012.2217750
Darby, 2011, RTO: An overview and assessment of current practice, Journal of Process Control, 21, 874, 10.1016/j.jprocont.2011.03.009
Ding, 2011, Offline modelling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization, IEEE Transactions on Neural Networks, 22, 408, 10.1109/TNN.2010.2102362
Ding, 2012, Knowledge-based plant-wide dynamic operation of mineral processing under uncertainty, IEEE Transactions on Industry Informatics, 8, 849, 10.1109/TII.2012.2205394
Engell, 2007, Feedback control for optimal process operation, Journal of Process Control, 17, 203, 10.1016/j.jprocont.2006.10.011
Findeisen, 1980
Hartmann, J. C. M. (1998). Distinguish between scheduling and planning models. Hydrocarbon processing (pp. 93–100) [July].
Hasikos, 2009, Operational optimization and real-time control of fuel-cell systems, Journal of Power Sources, 193, 258, 10.1016/j.jpowsour.2009.01.048
Jaschke, 2011, NCO tracking and self-optimizing control in the context of real-time optimization, Journal of Process Control, 21, 1047, 10.1016/j.jprocont.2011.07.001
Li, 2001, Hybrid intelligent control strategy supervising a DCS-controlled batch process, IEEE Control Systems Magazine, 21, 36, 10.1109/37.924796
Liu, 2011, Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes, IEEE Transactions on Neural Networks, 22, 2284, 10.1109/TNN.2011.2167686
Ma, 2012, Demand reduction in building energy systems based on economic model predictive control, Chemical Engineering Science, 67, 92, 10.1016/j.ces.2011.07.052
Marlin, T. E., & Hrymak, A. N. (1997). Real-time operations and optimization of continuous processes. In J. Kantor, et al., Fifth international conference on chemical process control, AIChE symp. Series 316, New York.
Mehmet, 2008, Real-time optimization of the pulp mill benchmark problem, Computers and Chemical Engineering, 32, 789, 10.1016/j.compchemeng.2007.03.004
Nath, 2000, On-line dynamic optimization of olefins plants, Computers & Chemical Engineering, 24, 533, 10.1016/S0098-1354(00)00525-1
Qin, 2003, A survey of industrial model predictive control technology, Control Engineering Practice, 11, 733, 10.1016/S0967-0661(02)00186-7
Qin, 2006, Semiconductor manufacturing process control and monitoring: A fab-wide framework, Journal of Process Control, 16, 179, 10.1016/j.jprocont.2005.06.002
Riccardo, 2009, Architectures for distributed and hierarchical model predictive control – A review, Journal of Process Control, 19, 723, 10.1016/j.jprocont.2009.02.003
Skogestad, 2000, Plantwide control: The search for the self-optimizing control structure, Journal of Process Control, 10, 487, 10.1016/S0959-1524(00)00023-8
Tatjewski, 2008, Advanced control and on-line process optimization in multilayer structures, Annual Reviews in Control, 32, 71, 10.1016/j.arcontrol.2008.03.003
Wang, 2004, Optimal-setting control for complicated industrial processes and its application study, Control Engineering Practice, 12, 65, 10.1016/S0967-0661(02)00316-7
Wu, 2009, Integrated intelligent control of gas mixing-and-pressurization process, IEEE Transactions on Control Systems Technology, 17, 68, 10.1109/TCST.2008.922563
Wu, 2010, Soft sensing method for magnetic tube recovery ratio via fuzzy systems and neural networks, Neurocomputing, 73, 2489, 10.1016/j.neucom.2009.12.036
Wu, 2009, Intelligent integrated optimization and control system for lead–zinc sintering process, Control Engineering Practice, 17, 280, 10.1016/j.conengprac.2008.07.007
Yan, 2006, Multivariable intelligent optimizing control approach for shaft furnace roasting process, Acta Automatica Sinica, 32, 636
Yang, 2009, A two-stage intelligent optimization system for the raw slurry preparing process of alumina sintering production, Engineering Applications of Artificial Intelligence, 22, 786, 10.1016/j.engappai.2008.11.003
Yu, 2011, Multi-objective production planning optimization using hybrid evolutionary algorithms for mineral processing, IEEE Transactions on Evolutionary Computation, 15, 487, 10.1109/TEVC.2010.2073472
Zhou, 2013, Intelligence-based supervisory control for optimizing the operation of a DCS-controlled grinding system, IEEE Transactions on Control Systems Technology, 21, 162, 10.1109/TCST.2012.2182996
Zhou, 2009, Intelligent optimal-setting control for grinding circuits of mineral processing process, IEEE Transactions on Automation Science and Engineering, 6, 730, 10.1109/TASE.2008.2011562